This project will address two methodological problems of mapping non-communicable diseases with long latencies in ecological studies. These problems are disease rates as well as onto potential risk factors and the identification of immanent spatial factors inducing either disease hot spots or clinical clusters. Both problems can be addressed analytically within the generalized least squares framework by incorporating explicitly the underlying spatial relationships. A migration process can be specified in the ecological regression context as a simultaneous autoregressive spatio-temporal process. A set of spatially relevant eigenvectors, which were extracted from the interregional migration flow matrix, have the potential to capture and filter the inherent autoregressive migration process. Any ecological disease model, which corrects for migration effects by spatial filtering, will give unbiased estimates with respect to underlying migratory process. A properly specified ecological disease model, which incorporates the proposed migration filter, as well as additional control variables and potential risk factors, may still exhibit local hot spots and clinical clusters in its regression residuals. An analysis of these spatial abnormalities in the unexplained component of the model may enhance our understanding of the intrinsic disease etiology with respect to unobserved environmental factors. However, inherent random noise may also being responsible for these spatial abnormalities. In order to gain epidemiological confidence in the hypothesized spatial factors, these spatial abnormalities must be consistent among related diseases sharing either a cognate etiology or being measured in comparable sub-populations. A statistical technique to test a set of several residual map patterns for spatial consistency in either local hot spots or clinical clusters is proposed. Significant findings from this method will guide our search for hidden spatial risk factors. The merits of both methods will be demonstrated on selected cancer maps from the newly released Cancer Atlas . The migration eigenvectors will become public domain. In addition, the proposed statistical procedure of residual map pattern comparison in clinical clusters and hot spots will be implemented within a Geographic Information System. Both methods will enable research teams to adjust for migration effects and to identify hidden risk factors in ecological studies of the Atlas of Cancer Mortality in the United States.